Fare classes based on an unconstrained demand forecast

Abstract

A computer-based system for unobscuring and/or unconstraining demand is disclosed. Via use of the system, actual airline seat bookings may be restated in an unobscured and/or unconstrained form, facilitating improved demand forecasts for subsequent seat bookings. In this manner, seat protects may be better allocated to align with actual demand, thus increasing revenue.

We claim: 
     
       1. A method comprising:
 determining, by one or more processors, one or more fare classes in a plurality of fare classes for which demand is constrained due to bookings in the one or more fare classes being restricted by availability in the one or more fare classes and due to the demand being obscured due to the one or more fare classes having bookings that are restricted due to the availability of the same seats in a lower fare class of the plurality of fare classes in an unobscured bookings table; 
 calculating, by the one or more processors, an unconstrained demand for each constrained fare class in the plurality of fare classes; 
 converting, by the one or more processors, the unconstrained demand for each fare class into integer values representing the seat bookings in the respective fare classes; 
 updating, by the one or more processors, the unobscured bookings table with the integer values to form an unconstrained bookings table; 
 generating, by the one or more processors, an unconstrained demand forecast for a flight based on the unconstrained bookings table; and 
 opening, by the one or more processors, an additional fare class on the flight for the seat bookings, based on the unconstrained demand forecast. 
 
     
     
       2. The method of  claim 1 , wherein the determining the one or more fare classes for which demand is constrained comprises identifying the one or more fare classes that were opened for the seat bookings during a particular time interval. 
     
     
       3. The method of  claim 1 , further comprising weighting, by the one or more processors, the unobscured demand for the fare class by a weighting parameter that is determined based at least partially on a departure date of the flight. 
     
     
       4. The method of  claim 1 , further comprising:
 refining, by the one or more processors using an unconstraining module, the unconstrained demand forecast, based on the unobscured demand; 
 opening, by the one or more processors, an additional fare class on the flight for seat bookings, based on the unconstrained demand forecast; 
 quantifying, by the one or more processors using a fare adjustment module, an impact of the opening of the additional fare class; 
 revising, by the one or more processors using the fare adjustment module, the additional fare class; 
 receiving, by the one or more processors interfacing with the revenue management system, the impact and the revising of the additional fare class; 
 revising, by the one or more processors interfacing with the revenue management system, fare class seat prices based on the impact and the revising of the additional fare class; and 
 revising, by the one or more processors interfacing with the passenger revenue optimization system, a demand forecast based on the impact and the revising of the additional fare class. 
 
     
     
       5. The method of  claim 1 , further comprising:
 determining, by the one or more processors, which of the one or more fare classes in the plurality of fare classes have demand that is obscured due to an availability of seats for booking in a lower fare class in the plurality of fare classes; 
 calculating, by the one or more processors iteratively executing an expectation maximization (EM) algorithm, an unobscured demand for a fare class in the plurality of fare classes; 
 generating, by the one or more processors with a demand forecasting system and using an unobscured bookings table, an unobscured demand forecast for a flight; and 
 opening, by the one or more processors, an additional fare class on the flight for seat bookings, based on the unobscured demand forecast. 
 
     
     
       6. The method of  claim 5 , further comprising:
 converting, by the one or more processors, the unobscured demand for the fare class into integer values representing the seat bookings in respective fare classes; and 
 updating, by the one or more processors, a bookings table with the integer values to form an unobscured bookings table. 
 
     
     
       7. The method of  claim 5 , wherein the plurality of fare classes are configured with parent-child relationships, and
 wherein, in the calculating the unobscured demand: 
 the unobscured demand for a child class is used as the upper bound for an EM algorithm as applied to a parent class, 
 top-down bookings for a class in question are used as the lower bound for the EM algorithm as applied to the parent class, and 
 the EM algorithm is iteratively executed by the processor until a convergent solution for the unobscured demand is obtained for the fare class. 
 
     
     
       8. The method of  claim 5 , wherein the unobscured bookings table is formed from updating the bookings table with integer values. 
     
     
       9. The method of  claim 5 , further comprising combining, by the one or more processors, an unobscured bookings table with an historical bookings table to form a revised historical bookings table. 
     
     
       10. The method of  claim 5 , wherein the unobscured demand forecast is used by at least one of a reservation system or a revenue management system to determine whether to open the additional fare class for the seat bookings. 
     
     
       11. The method of  claim 5 , wherein the determining the one or more fare classes for which demand is obscured further comprises:
 converting, by the one or more processors, a bookings table into a top-down format; 
 identifying, by the one or more processors, a lowest fare class for which the seat bookings were received in a first time period; 
 identifying as an obscured class, by the one or more processors, a parent class of the lowest fare class for which bookings were received; and 
 identifying as the obscured class, by the one or more processors, the one or more fare classes in the plurality of fare classes that are higher than the parent class of the lowest fare class for which the seat bookings were received. 
 
     
     
       12. The method of  claim 7 , wherein the EM algorithm comprises:
   expBkgs=mean+(stdev*stdev)*(pdfLower−pdfUpper)/(cdfUpper−cdfLower); where:
 
 the expBkgs is an expectation of the seat bookings in a current fare class known as unobscured seat bookings; 
 an upperBound is an upper bound of the unobscured seat bookings in the current fare class; 
 a lowerBound is a lower bound of the unobscured bookings in a current class; 
 the mean is a mean of the seat bookings for the current fare class in a cohort to which the flight belongs; 
 the stdev is a standard deviation of the seat bookings for the current fare class in the cohort to which the flight belongs; 
 the cdfUpper is a cumulative distribution function of the upperBound; 
 the cdfLower is a cumulative distribution function of the lowerBound; 
 the pdfUpper is a probability density function of the upperBound; and 
 the pdfLower is a probability density function of the lowerBound. 
 
     
     
       13. The method of  claim 5 , wherein the calculating the unobscured demand for the fare class is terminated upon determining, by the processor, that the unobscured demand for a current fare class rounds to zero. 
     
     
       14. The method of  claim 5 , further comprising weighting, by the one or more processors, the unobscured demand for the fare class by a weighting parameter. 
     
     
       15. The method of  claim 14 , wherein the weighting parameter is determined based at least partially on a departure date of the flight. 
     
     
       16. The method of  claim 5 , further comprising:
 storing, by the one or more processors, the unobscured demand forecast in data tables; 
 associating, by the one or more processors, the unobscured demand forecast with the one or more fare classes in the unobscured bookings table; 
 using, by the one or more processors, a key field in the unobscured bookings table to speed searches by simplifying the lookup and reducing in/out bottlenecks; 
 tuning, by the one or more processors, the unobscured bookings table to optimize database performance; 
 placing, by the one or more processors, frequently used indexes on separate file systems to further reduce the in/out bottlenecks; 
 conducting, by the one or more processors, sequential searches through the unobscured bookings table; and 
 sorting, by the one or more processors, records in the unobscured bookings table according to a known order to simplify lookup; and 
 obtaining, by the one or more processors, the unobscured demand forecast from the data tables. 
 
     
     
       17. The method of  claim 5 , further comprising
 accessing, by the one or more processors, a bookings table, wherein the bookings table further contains forecasted demand for the flight, and wherein the fare class in the plurality of fare classes is closed and contains none of the seat bookings; 
 determining, by the one or more processors and using the seat bookings and the forecasted demand, a monetary stimulation value of opening a closed class; 
 determining, by the one or more processors and using the seat bookings and the forecasted demand, a monetary dilution penalty of opening the closed class; and 
 generating, by the one or more processors, booking instructions based on the monetary stimulation value and the monetary dilution penalty. 
 
     
     
       18. The method of  claim 5 , further comprising:
 accessing, by the one or more processors, a bookings table, wherein the bookings table further contains information representing a net prime class seat booking for the flight, and wherein the net prime class seat booking occurred during a first time period; 
 determining, by the one or more processors, a floor class for the net prime class seat booking in the bookings table; 
 determining, by the one or more processors, a displacement class for a net prime class seat bookings in the bookings table; 
 determining, by the one or more processors, a target class for the net prime class seat bookings in the bookings table, wherein the target class is the higher of the floor class and the displacement class; and 
 remapping, by the one or more processors, the net prime class seat bookings in the bookings table into the target class to form a remapped bookings table. 
 
     
     
       19. The method of  claim 5 , further comprising:
 receiving, by the one or more processors, booking requests for the seat bookings in the additional fare class on the flight; and 
 issuing, by the one or more processors, tickets for the seat bookings that provide access to the flight and access to seats associated with the seat bookings. 
 
     
     
       20. A system comprising:
 one or more processors; and 
 one or more tangible, non-transitory memories configured to communicate with the one or more processors, 
 the one or more tangible, non-transitory memories having instructions stored thereon that, in response to execution by the one or more processors, cause the one or more processors to perform operations comprising:
 determining, by the one or more processors, one or more fare classes in a plurality of fare classes for which demand is constrained due to bookings in the one or more fare classes being restricted by availability in the one or more fare classes and due to the demand being obscured due to the one or more fare classes having bookings that are restricted due to the availability of the same seats in a lower fare class of the plurality of fare classes in an unobscured bookings table; 
 calculating, by the one or more processors, an unconstrained demand for each constrained fare class in the plurality of fare classes; 
 converting, by the one or more processors, the unconstrained demand for each fare class into integer values representing the seat bookings in the respective fare classes; 
 updating, by the one or more processors, the unobscured bookings table with the integer values to form an unconstrained bookings table; 
 generating, by the one or more processors, an unconstrained demand forecast for a flight based on the unconstrained bookings table; and 
 opening, by the one or more processors, an additional fare class on the flight for the seat bookings, based on the unconstrained demand forecast.